The following is a Global map of Reynolds OI.v2 Sea Surface Temperature (SST) anomalies for November 2016. It was downloaded from the KNMI Climate Explorer. The contour range was set to -2.5 to +2.5 deg C and the anomalies are referenced to the WMO-preferred period of 1981-2010 for short-term data.

November 2016 Sea Surface Temperature (SST) Anomalies Map

(Global SST Anomaly = +0.27 deg C)

MONTHLY GLOBAL OVERVIEW

The global Sea Surface Temperature anomaly for November 2016 shows a noticeable decline since October. A sizeable downtick in the Northern Hemisphere (-0.10 deg C) was suppressed by a lesser downtick in the Southern Hemisphere (-0.03 deg C). The noticeable drop in the Northern Hemisphere was driven by the North Pacific, which showed a sizeable decline (-0.21 deg C)…a response to the large ribbon of below normal sea surface temperature anomalies stretching across the extratropical North Pacific. (See the post Something to Keep an Eye On – The Large Blue Ribbon of Below-Normal Sea Surface Temperatures in the North Pacific.) Monthly sea surface temperature anomalies for the NINO3.4 show weakening La Niña conditions.

The monthly Global Sea Surface Temperature anomalies are presently at +0.27 deg C, referenced to the WMO-preferred base years of 1981 to 2010.

(1)Global Sea Surface Temperature Anomalies

Monthly Change = -0.06 deg C

THE EQUATORIAL PACIFIC

The monthly NINO3.4 Sea Surface Temperature anomalies for November 2016 have weakened and are rapidly approaching the threshold of ENSO-neutral conditions (not El Niño, not La Niña). They were at -0.54 deg C, an increase of about +0.21 deg C since October. (Also see the Weekly data shown near the end of the post.)

(2) NINO3.4 Sea Surface Temperature Anomalies

(5S-5N, 170W-120W)

Monthly Change = +0.21 deg C

####################################

The sea surface temperature anomalies for the NINO3.4 region in the east-central equatorial Pacific (5S-5N, 170E-120E) are a commonly used index for the strength, frequency and duration of El Niño and La Nina events. We keep an eye on the sea surface temperatures there because El Niño and La Niña events are the primary cause of the yearly variations in global sea surface temperatures AND they are the primary cause of the long-term warming of global sea surface temperatures over the past 30+ years. See the discussion of the East Pacific versus the Rest-of-the-World that follows.We present NINO3.4 sea surface temperature anomalies in monthly and weekly formats in these updates.

Also see the weekly values toward the end of the post.

INITIAL NOTES

Note 1: I’ve downloaded the Reynolds OI.v2 data from the KNMI Climate Explorer, using the base years of 1981-2010. The updated base years help to reduce the seasonal components in the ocean-basin subsets—they don’t eliminate those seasonal components, but they reduce them.

Note 4: I recently added a graph of the sea surface temperature anomalies for The Blob in the eastern extratropical North Pacific. It also is toward the end of the post. It will be removed after the November 2016 post.

The East Pacific Ocean also stands out in the trend map linked above. Some portions of its surfaces warmed and others cooled. It comes as no surprise then that the linear trend of the East Pacific (90S-90N, 180-80W) Sea Surface Temperature anomalies is so low since the start of the Reynolds OI.v2 composite through 2013. (See the graph here from the December 2013 update.) With the strong 2015/16 El Nino conditions in the eastern tropical Pacific, and with The Blob in 2013, 2014 and 2015, it has acquired a slight positive trend, but it’s still far below the approximate +0.15 deg C/decade warming rate predicted by the CMIP5 climate models. Please see Figure 19 in the post Maybe the IPCC’s Modelers Should Try to Simulate Earth’s Oceans. (Note that the region also includes portions of the Arctic and Southern Oceans.) That is, there has been little warming of the sea surfaces of the East Pacific (from pole to pole) in 3-plus decades. The East Pacific is not a small region. It represents about 33% of the surface area of the global oceans.

That leaves the largest region of the trend map, which includes the South Atlantic, the Indian and West Pacific Oceans, with the corresponding portions of the Arctic and Southern Oceans. Sea surface temperatures there warmed in very clear steps, in response to the significant 1986/87/88 and 1997/98 El Niño/La Niña events. It also appears as though the sea surface temperature anomalies of this subset have made another upward shift in response to the 2009/10 El Niño and 2010/11 La Niña events. I further described the ENSO-related processes that cause these upward steps in the recent post Answer to the Question Posed at Climate Etc.: By What Mechanism Does an El Niño Contribute to Global Warming?

As you’ll note, the values for the South Atlantic, Indian and West Pacific Oceans appear now to be responding to the 2014/15/16 El Nino. And it appears have seen another El Niño-related uptick.

NOTE: I have updated the above illustration and following discussion, because NOAA has recently revised their Oceanic NINO Index…once again. They’ve used the base years of 1986-2015 for the most recent data, which has resurrected the 2014/15 El Niño.

The periods used for the average temperature anomalies for the South Atlantic-Indian-West Pacific subset between the significant El Niño events of 1982/83, 1986/87/88, 1997/98, 2009/10 and 2015/16 are determined as follows. Using the most recent NOAA Oceanic Nino Index (ONI) for the official months of those El Niño events, I shifted (lagged) those El Niño periods by six months to accommodate the lag between NINO3.4 SST anomalies and the response of the South Atlantic-Indian-West Pacific Oceans, then deleted the South Atlantic-Indian-West Pacific values that correspond to those significant El Niño events. I then averaged the South Atlantic-Indian-West Pacific Oceans sea surface temperature anomalies between those El Niño-related gaps.

You’ll note I’ve ended the updates for the period after the 2009-10 El Niño. That was done to accommodate the expected response to the 2015/16 El Niño.

The Sea Surface Temperature anomalies of the East Pacific Ocean, or approximately 33% of the surface area of the global oceans, have shown comparatively little long-term warming since 1982 based on the linear trend. And between upward shifts, the Sea Surface Temperature anomalies for the South Atlantic-Indian-West Pacific subset (about 52.5% of the global ocean surface area) remain relatively flat, though they actually cool slightly. Anthropogenic forcings are said to be responsible for most of the rise in global surface temperatures over this period, but the Sea Surface Temperature anomaly graphs of those regions discussed above prompt a two-part question: Since 1982, what anthropogenic global warming processes would overlook the sea surface temperatures of 33% of the global oceans and have an impact on the other 52% but only during the months of the significant El Niño events of 1986/87/88, 1997/98 and 2009/10?

The MONTHLY graphs illustrate raw monthly OI.v2 sea surface temperature anomalies from November 1981 to November 2016, as it is presented by the KNMI Climate Explorer. While NOAA uses the base years of 1971-2000 for this product, those base years cannot be used at the KNMI Climate Explorer because they extend before the start year of the product. (NOAA had created a special climatology for the Reynolds OI.v2 product.) I’ve referenced the anomalies to the period of 1981 to 2010, which is actually 1982 to 2010 for most months.

MODEL-DATA COMPARISON: To counter the nonsensical “Just what AGW predicts” rantings of alarmists about the “record-high” global sea surface temperatures in 2014 and 2015, I’ve added a model-data comparison of satellite-era global sea surface temperatures to these monthly updates. See the example below. The models are represented the multi-model ensemble-member mean of the climate models stored in the CMIP5 archive, which was used by the IPCC for their 5th Assessment Report. For further information on the use of the model mean, see the post here. For most models, historic forcings run through 2005 (2012 for others) and the middle-of-the-road RCP6.0 forcings are used after in this comparison. The data are represented by NOAA’s Optimum Interpolation Sea Surface Temperature data, version 2—a.k.a. Reynolds OI.v2—which is NOAA’s best. The model outputs and data have been shifted so that their trend lines begin at “zero” anomaly for the (November, 1981) start month of this composite. That “zeroing” helps to highlight how poorly the models simulate the warming of the ocean surfaces…noticeably higher than the observed warming rate. Both the Reynolds OI.v2 values and the model outputs of their simulations of sea surface temperature (TOS) are available to the public at the KNMI Climate Explorer.

Note: I’ve changed the coordinates for The Blob to 40N-50N, 150W-130W to agree with those used in the NOAA/NCEP Monthly Ocean Briefing. I had been using the coordinates of 35N-55N, 150W-125W for The Blob.

HURRICANE MAIN DEVELOPMENT REGION

The sea surface temperatures of the tropical North Atlantic are one of the primary factors that contribute to the development and maintenance of hurricanes. I’ve recently added to the update the sea surface temperatures and anomalies for the hurricane main development region of the North Atlantic. It is often represented by the coordinates of 10N-20N, 80W-20W. While hurricanes tend to form there, they can also form outside it. Like June 2016, while sea surfaces for the main development region are warmer than “normal”.

(17) Hurricane Main Development Region

(10N-20N, 80W-20W)

Monthly Change (Anomalies) = +0.23 deg C

I’ve also included a graph of the November sea surface temperatures (not anomalies) for the Main Development Region. It confirms that sea surface temperatures there are near to being at record highs and that sea surface temperatures of the Main Development Region are above the 26 deg C threshold for hurricane formation, as they normally are during November.

INTERESTED IN LEARNING MORE ABOUT HOW DATA SUGGEST THE GLOBAL OCEANS WARMED NATURALLY?

Why should you be interested? The hypothesis of manmade global warming depends on manmade greenhouse gases being the cause of the recent warming. But the sea surface temperature record indicates El Niño and La Niña events are responsible for the warming of global sea surface temperature anomalies over the past 32 years, not manmade greenhouse gases. Scroll back up to the discussion of the East Pacific versus the Rest of the World. I’ve searched sea surface temperature records for more than 4 years, and I can find no evidence of an anthropogenic greenhouse gas signal. That is, the warming of the global oceans has been caused by Mother Nature, not anthropogenic greenhouse gases.

My e-book (pdf) about the phenomena called El Niño and La Niña is titled Who Turned on the Heat? with the subtitle The Unsuspected Global Warming Culprit, El Niño Southern Oscillation. It is intended for persons (with or without technical backgrounds) interested in learning about El Niño and La Niña events and in understanding the natural causes of the warming of our global oceans for the past 30 years. Because land surface air temperatures simply exaggerate the natural warming of the global oceans over annual and multidecadal time periods, the vast majority of the warming taking place on land is natural as well. The book is the product of years of research of the satellite-era sea surface temperature data that’s available to the public via the internet. It presents how the data accounts for its warming—and there are no indications the warming was caused by manmade greenhouse gases. None at all.

The monthly Sea Surface Temperature (SST) anomalies, the map and model outputs used in this post are available from the KNMI Climate Explorer.

]]>https://bobtisdale.wordpress.com/2016/12/06/november-2016-sea-surface-temperature-sst-anomaly-update/feed/9bobtisdale00-global-ssta-map01-global-ssta02-nino3-4-ssta03-east-pac-ssta04-s-atl-indian-w-pac-ssta05-n-hem-ssta06-s-hem-ssta07-n-atl-ssta08-s-atl-ssta09-pac-ssta10-n-pac-ssta11-s-pac-ssta12-indian-ssta13-arctic-ssta14-southern-ssta15-weekly-nino3-4-ssta000-model-data-global-ssta16-the-blob-ssta17-mdr-sstaEarly December 2016 La Niña Update: Mixed Signals from NOAA and BOMhttps://bobtisdale.wordpress.com/2016/12/05/early-december-2016-la-nina-update-mixed-signals-from-noaa-and-bom/
https://bobtisdale.wordpress.com/2016/12/05/early-december-2016-la-nina-update-mixed-signals-from-noaa-and-bom/#commentsMon, 05 Dec 2016 15:07:44 +0000http://bobtisdale.wordpress.com/?p=11425Continue reading →]]>Note: See Update at the end of the post.

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Last month on November 10, NOAA issued a La Niña Advisory, indicating weak La Niña conditions existed and that those conditions were “slightly favored to persist (~55% chance) through winter 2016-17.” Let’s see how things are progressing.

The sea surface temperature anomalies of the NINO3.4 region of the tropical Pacific (coordinates 5S-5N, 170W-120W) are a commonly used index for the timing, strength and duration of El Niño and La Niña events.

Note that the horizontal green line is the most recent weekly value, not a trend line.

This data are based on NOAA’s original version of their Reynolds OI.v2 satellite-enhanced sea surface temperature dataset. The anomalies are referenced to the base period of 1981-2010. This is not the dataset that NOAA uses for their “official” ENSO indices.

NOAA’S MONTHLY IN SITU-ONLY ERSST.v4 SEA SURFACE TEMPERATURE DATA (WITH A FIXED SET OF BASE YEARS FOR ANOMALIES, 1981-2010) SHOW TEMPERATURES WELL WITHIN WEAK LA NIÑA CONDITIONS FOR NOVEMBER AND STRENGTHENING SLIGHTLY

The monthly ERSST.v4-based data for November 2016 show NINO3.4 sea surface temperature anomalies well within the realm of weak La Niña conditions, at -0.82 deg C. See Figure 2. The October value was -0.8 deg C.

As opposed to using a fixed 30-year based period for the ERSST.v4-based NINO3.4 anomalies in their “official” Oceanic NINO Index, NOAA uses multiple 30-year periods that shift every 5 years. See the NOAA explanation here. NOAA claims they’ve taken this curious approach “to remove this [global] warming trend” on the equatorial Pacific sea surface temperature data. We revealed, however, in the 2012 post Comments on NOAA’s Recent Changes to the Oceanic NINO Index (ONI) that the global “warming trend” in NINO3.4 sea surface temperature data resulted primarily from the impact of the well-known and naturally occurring 1976 Pacific climate shift. Apparently, NOAA does (oops) doesn’t want mother nature to be responsible for even localized warming. This, of course, renders the Oceanic NINO Index useless for realistic climate studies.

Regardless, NOAA has adopted this odd approach to calculate the sea surface temperature anomaly values for their “official” Ocean NINO Index. The monthly NINO3.4 values that are input to the Ocean NINO Index are shown in Figure 3. The November 2016 NINO3.4 sea surface temperature “anomaly” for this altered dataset is -0.92 deg C, which is approaching the -1.0 deg C threshold of a moderately strong La Niña. From October to November 2016, this modified dataset shows a noticeable strengthening of -0.05 deg C.

Figure 3

So it appears that NOAA is working hard at making the 2016/17 La Niña an “official” reality.

Note: NOAA then uses a 3-month running average of this altered monthly NINO3.4-based data for their Oceanic NINO Index.

THE SOUTHERN OSCILLATION INDEX FROM AUSTRALIA’S BOM CONTINUES TO SHOW ENSO NEUTRAL CONDITIONS

The Southern Oscillation Index (SOI) from Australia’s Bureau of Meteorology is another widely used reference for the strength, frequency and duration of El Niño and La Niña events. We discussed the Southern Oscillation Index in Part 8 of the 2014/15 El Niño series. It is derived from the sea level pressures of Tahiti and Darwin, Australia, and as such it reflects the wind patterns off the equator in the southern tropical Pacific. With the Southern Oscillation Index, El Niño events are strong negative values and La Niñas are strong positive values, which is the reverse of what we see with sea surface temperature-based indices. The November 2016 Southern Oscillation Index shows ENSO neutral conditions exist in the tropical Pacific…with a value is -0.7, which is the sign opposite to those of La Niña conditions. (The BOM threshold for La Niña conditions is an SOI value of +8.0.) According to the SOI, we briefly made it into La Niña conditions in September and since then, ENSO neutral. Figure 4 presents a time-series graph of the SOI data.

Figure 4

Again, the horizontal green line is the most recent monthly value, not a trend line.

I published On Global Warming and the Illusion of Control (25MB .pdf) back in November 2015. The introductory post is here. That 700+ page climate change reference is free. Chapter 3.7 includes detailed discussions of El Niño events and their aftereffects…though not as detailed as in Who Turned on the Heat?

My ebook Who Turned on the Heat? – The Unsuspected Global Warming Culprit: El Niño-Southern Oscillation (23MB .pdf) goes into a tremendous amount of detail to explain El Niño and La Niña processes and the long-term global-warming aftereffects of strong El Niño events. It too is free. See the introductory post here. Who Turned on the Heat? weighs in at a whopping 550+ pages, about 110,000+ words. It contains somewhere in the neighborhood of 380 color illustrations. In pdf form, it’s about 23MB. It includes links to more than a dozen animations, which allow the reader to view ENSO processes and the interactions between variables.

UPDATE:

Within hours of my publishing this post, Australia’s BOM has cancelled their La Niña watch. They write in their December 6thENSO Update:

La Niña no longer likely in the coming months

The El Niño–Southern Oscillation (ENSO) in the tropical Pacific Ocean remains neutral (neither El Niño nor La Niña). Although some very weak La Niña-like patterns continue (such as cooler than normal ocean temperatures and reduced cloudiness in the central and eastern Pacific), La Niña thresholds have not been met. Climate models and current observations suggest these patterns will not persist. The likelihood of La Niña developing in the coming months is now low, and hence the Bureau’s ENSO Outlook has shifted from La Niña WATCH to INACTIVE.

In 1988, the United Nations, a political body, founded the global-warming-report-writing entity called the Intergovernmental Panel on Climate Change (IPCC). The IPCC was created to support political agendas. And in 1995, politics corrupted climate science, when politicians changed the language of the IPCC’s second assessment report, eliminating the scientists’ statements of uncertainties. To this day, the climate science community still cannot truly differentiate between natural and anthropogenic global warming. Why? The climate models used in attribution studies still cannot simulate modes of natural variability that can cause global warming over multidecadal timeframes.

INTRODUCTION

President Elect Donald Trump’s skepticism of human-induced global warming/climate change had been one of the focuses of the mainstream media during the U.S. elections and remains so in the minds of many environmentalists and their associates in the media. A plethora of articles and talking-head clips have been published and broadcast, bringing the political nature of climate science to the public eye once again.

In the early 1990s I was visiting the White House Science Advisor, Sir Prof. Dr. Robert Watson, who was pontificating on how we had successfully regulated Freon to solve the ozone depletion problem, and now the next goal was to regulate carbon dioxide, which at that time was believed to be the sole cause of global warming.

I was a little amazed at this cart-before-the-horse approach. It really seemed to me that the policy goal was being set in stone, and now the newly-formed United Nations Intergovernmental Panel on Climate Change (IPCC) had the rather shady task of generating the science that would support the policy.

THE SHADY TASK OF GENERATING THE SCIENCE TO SUPPORT POLICY

To reinforce Dr. Spencer’s cart-before-the-horse statement, I’m going to reproduce a portion of the Introduction to my free ebook, a 700+ page reference work, On Global Warming and the Illusion of Control – Part 1. This portion provides quotations from the United Nations and the Intergovernmental Panel on Climate Change, along with links to the referenced webpages. Under the heading of YOU’D BE WRONG IF YOU THOUGHT THE IPCC WAS A SCIENTIFIC BODY, I wrote in part:

The Intergovernmental Panel on Climate Change (IPCC) is a political entity, not a scientific one. The IPCC begins the opening paragraphs of its History webpage (my boldface):

The Intergovernmental Panel on Climate Change was created in 1988. It was set up by the World Meteorological Organization (WMO) and the United Nations Environment Program (UNEP) to prepare, based on available scientific information, assessments on all aspects of climate change and its impacts, with a view of formulating realistic response strategies. The initial task for the IPCC as outlined in UN General Assembly Resolution 43/53 of 6 December 1988 was to prepare a comprehensive review and recommendations with respect to the state of knowledge of the science of climate change; the social and economic impact of climate change, and possible response strategies and elements for inclusion in a possible future international convention on climate.

Thus, the IPCC was founded to write reports. Granted, they are very detailed reports, so burdensome that few persons read them in their entirety. Of the few people who read them, most only read the Summaries for Policymakers. But are you aware that the language of the IPCC Summary for Policymakers is agreed to by politicians during week-long meetings? A draft is written by the scientists for the politicians, but the politicians debate how each sentence is phrased and whether it is to be included in the summary. And those week-long political debates about the Summary for Policymakers are closed to the public.

Also from that quote above, we can see that the content of IPCC’s reports was intended to support an international climate-change treaty. That 1992 treaty is known as the United Nations Framework Convention on Climate Change (UNFCCC). A copy of the UNFCCC is available here. Under the heading of Article 2 – Objective, the UNFCCC identifies its goal as limiting the emissions of greenhouse gases (my boldface):

The ultimate objective of this Convention and any related legal instruments that the Conference of the Parties may adopt is to achieve, in accordance with the relevant provisions of the Convention, stabilization of greenhouse gas concentrations in the atmosphere at a level that would prevent dangerous anthropogenic interference with the climate system.

Because the objective of the UNFCCC treaty is to limit the emissions of man-made greenhouse gases, and because the goal of the IPCC is to prepare reports that supported the treaty, it safe to say the IPCC’s sole role is simply to write scientific reports that support the assumed need to limit greenhouse gas emissions. Hmmm. Do you think that focus might limit scientific investigation and understandings?

Later in the opening paragraph of the IPCC’s History webpage, they state (my boldface and caps):

Today the IPCC’s role is as defined in Principles Governing IPCC Work, “…to assess on a comprehensive, objective, open and transparent basis the scientific, technical and socio-economic information relevant to understanding the scientific basis of risk of HUMAN-INDUCED climate change, its potential impacts and options for adaptation and mitigation.

The fact that the IPCC has focused all of their efforts on “understanding the scientific basis of risk of human-induced climate change” is very important. The IPCC has never realistically tried to determine if natural factors could have caused most of the warming the Earth has experienced over the past century. For decades, they’ve worn blinders that blocked their views of everything other than the hypothetical impacts of carbon dioxide. The role of the IPCC has always been to prepare reports that support the reduction of greenhouse gas emissions caused by the burning of fossil fuels. As a result, that’s where all of the research money goes. The decision to only study human-induced global warming is a political choice, not a scientific one. And it’s a horrible choice.

As a result of that political choice, there is little scientific research that attempts to realistically determine how much of the warming we’ve experienced is attributable to natural factors. We know this is fact because the current generation of climate models—the most complex climate models to date—still cannot simulate naturally occurring ocean-atmosphere processes that can cause Earth’s surfaces (and the oceans to depth) to warm for multidecadal periods or stop that warming. Skeptics have confirmed those failings a number of times in blog posts. I even wrote a book about those failings, appropriately titled Climate Models Fail.

…

[End Reprint]

EVEN SHADIER: CHANGING THE SCIENCE TO SUPPORT POLICY

Were you aware that politicians revised the text of the IPCC’s second assessment report, drastically changing the draft written by the scientists? Once again, I’m reproducing a portion of my free ebook On Global Warming and the Illusion of Control – Part 1. It’s from the heading of THE EVOLUTION OF THE CATASTROPHIC ANTHROPOGENIC GLOBAL WARMING MOVEMENT:

While there were early scientific studies that pointed to possible increases in surface temperatures associated with the emissions of man-made greenhouse gases, let’s begin this discussion with the formation of the report-writing wing of the United Nations called the Intergovernmental Panel on Climate Change (IPCC). As discussed above, the primary task of the IPCC was to create reports that supported the politicians’ agendas. Limiting global warming was likely one of those focuses, but most assuredly there were many others.

The politicians found scientists to write those reports—so began the mutually beneficial relationship between climate scientists and politicians. The politicians wanted scientific support for their agendas and the scientists were more than willing to oblige because the politicians held the purse strings for climate research.

The first IPCC report in 1991 was inconclusive, inasmuch as the scientists could not differentiate between man-made and natural warming…

Note for this post: The Policymakers Summary for the IPCC’s first assessment report is here. There they write:

The size of this warming is broadly consistent with predictions of climate models, but it is also of the same magnitude as natural climate variability. Thus the observed increase could be largely due to this natural variability, alternatively this variability and other human factors could have offset a still larger human-induced greenhouse warming. The unequivocal detection of the enhanced greenhouse effect from observations is not likely for a decade or more.

So in 1991 the science community was not expecting to be able to differentiate between natural and anthropogenic global warming until 2001 at the earliest.

[End note.]

…In spite of those uncertain findings, a year later [in 1992] the politicians prepared a treaty called the United Nations Framework Convention on Climate Change with the intent of limiting global temperatures to 2 deg C above pre-industrial values—a limit that was first proposed in the mid-1970s by an economist, not a climate scientist.

Perhaps surprisingly, the idea that temperature could be used to guide society’s response to climate change was first proposed by an economist.

In the 1970s, Yale professor William Nordhaus alluded to the danger of passing a threshold of two degrees in a pair of now famous papers, suggesting that warming of more than two degrees would push the climate beyond the limits humans were familiar with:

“According to most sources the range of variation between distinct climatic regimes is on the order of ±5°C, and at present time the global climate is at the high end of this range. If there were global temperatures more than 2° of [sic] 3° above the current average temperature, this would take the climate outside of the range of observations which have been made over the last several hundred thousand years.”

…

In the early 1990s, the politicians continued to fling funds at scientists with hope the next report would provide support for their agendas. Much to the politicians’ astonishment, the scientists’ initial draft of the 1995 Summary for Policymakers for the 2nd Assessment Report from the IPCC was still inconclusive.

Imagine that. In 1992, the United Nations had convinced many countries around the globe to enter into a treaty to limit emissions of greenhouse gases, when a year before the IPCC could not find mankind’s fingerprint on global warming. Then, by 1995, the politicians’ scientific report-writing body, the IPCC, still could not differentiate between man-made and natural warming, and the climate scientists had stated that fact in the draft of the second IPCC assessment report. The politicians were between the rock and the hard place. They’d had a treaty in place for 3 years but their report-writing scientists could not find evidence to support it.

So, after most of the scientists had left the meeting, the politicians and a lone scientist changed the language of the second IPCC assessment report in a very subtle but meaningful way. Voila! The politicians and one scientist initiated what is now called the consensus. (See the 3-part, very detailed analysis by Bernie Lewin about the 1995 IPCC conference in Madrid. Part one is here.)

…

[End Reprint]

The three parts of the series by Bernie Lewin about the 1995 IPCC conference in Madrid are appropriately titled:

Bernie Levin writes about the draft of the IPCC’s second assessment report in Part 1 of his series (My boldface):

Alas, by the early autumn of 1995 the signs were not good. Although a draft leaked in September managed to say that the warming is unlikely to be entirely due to natural causes, this was hardly in dispute, and this was not exactly announcing imminent catastrophe. Moreover, there remained extraordinary strong caveats, especially in Chapter 8, to every positive conclusion. The draft that was circulated to the participants at the Madrid conference, and the only one available when the Report was finally ‘accepted’ by the meeting (see explanation in a following post), also stated in its introduction that results of recent studies point towards a human influence. This was the strongest statement yet, but the body of the document and the concluding summary were not so confident. Some of the boldest retractions were as follows:

Of Studies of Changes in Global Mean Variables (8.4.1): ‘While none of these studies has specifically considered the attribution issue, they often draw some attribution conclusions, for which there is little justification.’

Of the greenhouse signal in studies of modelled and observed spatial and temporal patterns of change (8.4.2.1): ‘none of the studies cited above has shown clear evidence that we can attribute the observed changes to the specific cause of increases in greenhouse gases.’

Of pattern studies ‘fingerprinting’ the global warming (see discussion in later post): While some of the pattern-base studies discussed have claimed detection of a significant climate change, no study to date has positively attributed all or part [of the climate change observed] to [anthropogenic ] causes. Nor has any study quantified the magnitude of a greenhouse gas effect or aerosol effect in the observed data—an issue of primary relevance to policy makers.

Of the overall level of uncertainty: Any claims of positive detection and attribution of significant climate change are likely to remain controversial until uncertainties in the total natural variability of the climate system are reduced.

Of the question: When will an anthropogenic effect on climate be identified? (8.6): It is not surprising that the best answer to this question is, `We do not know.’

[A copy of the 9Oct95 draft of Ch 8 has not been obtained. UPDATE 29June12: 9Oct draft obtained and changes have been verified]

The politicians didn’t like the uncertainties expressed in those statements, so they deleted them. Amazing! Were you aware that politicians had dictated climate science?

Important note: Keep in mind that Mount Pinatubo erupted in 1991, temporarily driving global surface temperatures downward. While temperatures rebounded by 1995 to a level that was slightly higher than in 1991, the volcanic aerosols spewed into the stratosphere by Mount Pinatubo had produced a noticeable drop in the warming rate since the mid-1970s start of the recent warming period. See Figure 1. That is, the global warming rate from 1975 to 1995 is noticeably lower than the trend from 1975 to 1991, as one would expect. (I’ve used the GISS dTs data in the top graph of Figure 1, because GISS did not begin to use sea surface temperature data in their global temperature data until 1995. I’ve included the GISS Land-Ocean Temperature Index in the lower graph as a reference. Both are current versions of the data)

Figure 1

So with the massive impact of Mount Pinatubo on global surface temperatures, one might think that the continued uncertainty by climate scientists was still warranted in 1995.

CLIMATE SCIENCE UNDER THE DIRECTION OF THE IPCC STILL CANNOT REALISTICALLY DIFFERENTIATE BETWEEN NATURAL AND HUMAN-INDUCED GLOBAL WARMING

One of the objectives of the climate science community under the direction of the IPCC has been to attribute most of the global warming since the mid-1970s to man-made causes. In other words, if Mother Nature was responsible for the warming, the political goal to limit the use of fossil fuels would have no foundation, and because the intent of the IPCC is to support political agendas, the climate science community had to be able to point to mankind as the culprit. The climate modelers achieved that goal using a few very simple tactics.

The first thing climate modelers did was they ignored the naturally occurring ocean-atmosphere processes that contribute to or suppress global warming. The climate models used by the IPCC still to this day cannot simulate those processes properly, and we’ll illustrate that fact very plainly later in this book. Ignoring Mother Nature’s contributions was the simplest and most-convenient way to show humans are responsible for the warming. The modelers also elected not to disclose this fact to the public when they presented their modeled-based attribution studies using the next tactic.

That tactic is a very simple and easy-to-understand way to falsely attribute most of the warming to mankind. The modelers had their climate model runs that showed virtual global surface temperatures warming in response to all the climate forcings that are used as inputs to the models. They then performed additional modeling experiments. Instead of using all of the climate forcings they typically include in their simulations of past climate, they only used the natural climate forcings of solar radiation and volcanic aerosols in the extra climate model runs. The flawed logic: if the models run with only solar radiation and volcanic aerosols (natural forcings) cannot simulate the warming we’ve experienced in the late 20th century, and if the models run with natural and anthropogenic forcings can simulate the warming, then the warming since the 1970s had to be caused by man-made greenhouse gases.

As an example, Figure 1.12-1 is a time-series graph that runs from 1880 to 2010. The solid brown curve shows the net radiative forcing of all forcings that are used as inputs to the climate models prepared by the Goddard Institute for Space Studies (GISS). They’re from the Forcings in GISS Climate Model webpage, specifically the table here. (In Chapter 2.3, we will illustrate the forcings individually.) Also included in Figure 1.12-1 is the net of only the solar irradiance (sunlight) and stratospheric aerosols (sunlight-blocking volcanic aerosols), shown as the dark green dashed curve; they are considered naturally occurring forcings. As we can see, the group with all of the forcings shows a long-term increase, while the combined forcings from the sun and volcanos do not.

# # #

The climate scientists then ran the additional model simulations with only the natural forcings. They then compare the model simulations using natural and man-made forcings with the models run with the natural forcings only. An example of one of those comparisons is shown in Figure 1.12-2. The models run with man-made and natural forcings show considerable warming in the late 20th Century and the models run with only natural forcings do not show the warming.

Note: The citation required by the IPCC for the use of their illustration is at the end of the chapter. [End note.]

About their FAQ10.1, Figure 1, the IPCC writes:

FAQ 10.1, Figure 1 illustrates part of a fingerprint assessment of global temperature change at the surface during the late 20th century. The observed change in the latter half of the 20th century, shown by the black time series in the left panels, is larger than expected from just internal variability. Simulations driven only by natural forcings (yellow and blue lines in the upper left panel) fail to reproduce late 20th century global warming at the surface with a spatial pattern of change (upper right) completely different from the observed pattern of change (middle right). Simulations including both natural and human-caused forcings provide a much better representation of the time rate of change (lower left) and spatial pattern (lower right) of observed surface temperature change.

Both panels on the left show that computer models reproduce the naturally forced surface cooling observed for a year or two after major volcanic eruptions, such as occurred in 1982 and 1991. Natural forcing simulations capture the short-lived temperature changes following eruptions, but only the natural + human caused forcing simulations simulate the longer-lived warming trend.

The caption for their FAQ 10.1, Figure reads:

FAQ 10.1, Figure 1 | (Left) Time series of global and annual-averaged surface temperature change from 1860 to 2010. The top left panel shows results from two ensemble [sic] of climate models driven with just natural forcings, shown as thin blue and yellow lines; ensemble average temperature changes are thick blue and red lines. Three different observed estimates are shown as black lines. The lower left panel shows simulations by the same models, but driven with both natural forcing and human-induced changes in greenhouse gases and aerosols. (Right) Spatial patterns of local surface temperature trends from 1951 to 2010. The upper panel shows the pattern of trends from a large ensemble of Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations driven with just natural forcings. The bottom panel shows trends from a corresponding ensemble of simulations driven with natural + human forcings. The middle panel shows the pattern of observed trends from the Hadley Centre/Climatic Research Unit gridded surface temperature data set 4 (HadCRUT4) during this period.

There are now a greater number of climate simulations from AOGCMs [Atmosphere-Ocean General Circulation Models] for the period of the global surface instrumental record than were available for the TAR [Third Assessment Report], including a greater variety of forcings in a greater variety of combinations. These simulations used models with different climate sensitivities, rates of ocean heat uptake and magnitudes and types of forcings (Supplementary Material, Table S9.1). Figure 9.5 shows that simulations that incorporate anthropogenic forcings, including increasing greenhouse gas concentrations and the effects of aerosols, and that also incorporate natural external forcings provide a consistent explanation of the observed temperature record, whereas simulations that include only natural forcings do not simulate the warming observed over the last three decades.

As mentioned earlier, the logic behind this type of attribution is very simple, childishly simple. If models that include anthropogenic and natural forcings can simulate the warming, and if the models that include only natural forcings cannot simulate the warming, then the anthropogenic forcings must be responsible for the global warming.

But the logic is flawed—fatally flawed. There are naturally occurring ocean-atmosphere processes that can cause global surface temperatures to warm and cool without being forced to do so by man-made greenhouse gases. The climate models do not simulate those processes so they are not considered in attribution studies like this.

There’s another way to look at this. One of the greatest climate-model failings is their inability to simulate naturally occurring ocean-atmosphere processes…like those associated with El Niño and La Niña events, like those associated with the Atlantic Multidecadal Oscillation. We’ll present those failings later in the book. So like anyone trying to market a flawed product, the crafty IPCC turned those failings into a positive by ignoring them in their attribution studies.

[End Reprint]

Yup, that’s a pretty pathetic way to attribute the recent bout of global warming to anthropogenic greenhouse gases.

CLOSING

Is President-elect Donald Trump correct to be skeptical of the politicized science behind hypothetical human-induced global warming/climate change? Of course, he is.

Climate science was politicized in 1988 when the UN’s politicians founded and provided direction to the Intergovernmental Panel on Climate Change, the IPCC. Climate science was corrupted by politics in 1995, more than 2 decades ago, when politicians changed the language of the second assessment report of the IPCC. And, of course, climate scientists still to this day cannot realistically attribute to manmade causes the global warming we’ve experienced since the 1970s, because climate models cannot simulate naturally occurring, naturally fueled coupled ocean-atmosphere processes that can cause global surfaces to warm over multidecadal timeframes. The fact that climate models cannot simulate any warming unless they are forced by numerical representations of manmade greenhouse gases is a model failing, not a means to credibly attribute global warming to the emissions of carbon dioxide. With climate science, the cart is still before the horse.

FINAL NOTE

I’ve searched online for the initial draft of the 1995 IPCC Second Assessment Report, but have been unable to locate it. If you know where it can be found, please leave a link in the comments. Thank you.

]]>https://bobtisdale.wordpress.com/2016/11/29/the-politicization-of-climate-science-is-not-a-recent-phenomenon/feed/3bobtisdalefigure-1figure-1-12-1figure-1-12-2figure-1-12-3October 2016 Global Surface (Land+Ocean) and Lower Troposphere Temperature Anomaly Updatehttps://bobtisdale.wordpress.com/2016/11/16/october-2016-global-surface-landocean-and-lower-troposphere-temperature-anomaly-update/
https://bobtisdale.wordpress.com/2016/11/16/october-2016-global-surface-landocean-and-lower-troposphere-temperature-anomaly-update/#commentsWed, 16 Nov 2016 11:07:12 +0000http://bobtisdale.wordpress.com/?p=11399Continue reading →]]>This post provides updates of the values for the three primary suppliers of global land+ocean surface temperature reconstructions—GISS through October 2016 and HADCRUT4 and NOAA NCEI (formerly NOAA NCDC) through September 2016—and of the two suppliers of satellite-based lower troposphere temperature composites (RSS and UAH) through October 2016. It also includes a few model-data comparisons.

This is simply an update, but it includes a good amount of background information for those new to the datasets. Because it is an update, there is no overview or summary for this post. There are, however, summaries for the individual datasets. So for those familiar with the datasets, simply fast-forward to the graphs and read the summaries under the heading of “Update”.

INITIAL NOTES:

We discussed and illustrated the impacts of the adjustments to surface temperature data in the posts:

The NOAA NCEI product is the new global land+ocean surface reconstruction with the manufactured warming presented in Karl et al. (2015). For summaries of the oddities found in the new NOAA ERSST.v4 “pause-buster” sea surface temperature data see the posts:

Even though the changes to the ERSST reconstruction since 1998 cannot be justified by the night marine air temperature product that was used as a reference for bias adjustments (See comparison graph here), and even though NOAA appears to have manipulated the parameters (tuning knobs) in their sea surface temperature model to produce high warming rates (See the post here), GISS also switched to the new “pause-buster” NCEI ERSST.v4 sea surface temperature reconstruction with their July 2015 update.

The UKMO also recently made adjustments to their HadCRUT4 product, but they are minor compared to the GISS and NCEI adjustments.

We’re using the UAH lower troposphere temperature anomalies Release 6.5 for this post even though it’s in beta form. And for those who wish to whine about my portrayals of the changes to the UAH and to the GISS and NCEI products, see the post here.

The GISS LOTI surface temperature reconstruction and the two lower troposphere temperature composites are for the most recent month. The HADCRUT4 and NCEI products lag one month.

Much of the following text is boilerplate that has been updated for all products. The boilerplate is intended for those new to the presentation of global surface temperature anomalies.

Most of the graphs in the update start in 1979. That’s a commonly used start year for global temperature products because many of the satellite-based temperature composites start then.

We discussed why the three suppliers of surface temperature products use different base years for anomalies in chapter 1.25 – Many, But Not All, Climate Metrics Are Presented in Anomaly and in Absolute Forms of my free ebook On Global Warming and the Illusion of Control – Part 1 (25MB).

Since the July 2015 update, we’re using the UKMO’s HadCRUT4 reconstruction for the model-data comparisons using 61-month filters.

For a continued change of pace, let’s start with the lower troposphere temperature data. I’ve left the illustration numbering as it was in the past when we began with the surface-based data.

UAH LOWER TROPOSPHERE TEMPERATURE ANOMALY COMPOSITE (UAH TLT)

Special sensors (microwave sounding units) aboard satellites have orbited the Earth since the late 1970s, allowing scientists to calculate the temperatures of the atmosphere at various heights above sea level (lower troposphere, mid troposphere, tropopause and lower stratosphere). The atmospheric temperature values are calculated from a series of satellites with overlapping operation periods, not from a single satellite. Because the atmospheric temperature products rely on numerous satellites, they are known as composites. The level nearest to the surface of the Earth is the lower troposphere. The lower troposphere temperature composite include the altitudes of zero to about 12,500 meters, but are most heavily weighted to the altitudes of less than 3000 meters. See the left-hand cell of the illustration here.

Note: RSS recently release new versions of the mid-troposphere temperature (TMT) and lower stratosphere temperature (TLS) products. So far, their lower troposphere temperature product has not been updated to this new version.

Introduction: The GISS Land Ocean Temperature Index (LOTI) reconstruction is a product of the Goddard Institute for Space Studies. Starting with the June 2015 update, GISS LOTI uses the new NOAA Extended Reconstructed Sea Surface Temperature version 4 (ERSST.v4), the pause-buster reconstruction, which also infills grids without temperature samples. For land surfaces, GISS adjusts GHCN and other land surface temperature products via a number of methods and infills areas without temperature samples using 1200km smoothing. Refer to the GISS description here. Unlike the UK Met Office and NCEI products, GISS masks sea surface temperature data at the poles, anywhere seasonal sea ice has existed, and they extend land surface temperature data out over the oceans in those locations, regardless of whether or not sea surface temperature observations for the polar oceans are available that month. Refer to the discussions here and here. GISS uses the base years of 1951-1980 as the reference period for anomalies. The values for the GISS product are found here. (I archived the former version here at the WaybackMachine.)

Update: The October 2016 GISS global temperature anomaly is +0.89 deg C. According to the GISS LOTI data, global surface temperature anomalies made a slight downtick in October, a -0.01 deg C decrease.

Figure 1 – GISS Land-Ocean Temperature Index

NCEI GLOBAL SURFACE TEMPERATURE ANOMALIES (LAGS ONE MONTH)

NOTE: The NCEI only produces the product with the manufactured-warming adjustments presented in the paper Karl et al. (2015). As far as I know, the former version of the reconstruction is no longer available online. For more information on those curious NOAA adjustments, see the posts:

Update (Lags One Month): The September 2016 NCEI global land plus sea surface temperature anomaly was +0.89 deg C. See Figure 2. It remained relatively flat (a decrease of about -0.01 deg C) since August 2016.

Figure 2 – NCEI Global (Land and Ocean) Surface Temperature Anomalies

UK MET OFFICE HADCRUT4 (LAGS ONE MONTH)

Introduction: The UK Met Office HADCRUT4 reconstruction merges CRUTEM4 land-surface air temperature product and the HadSST3 sea-surface temperature (SST) reconstruction. CRUTEM4 is the product of the combined efforts of the Met Office Hadley Centre and the Climatic Research Unit at the University of East Anglia. And HadSST3 is a product of the Hadley Centre. Unlike the GISS and NCEI reconstructions, grids without temperature samples for a given month are not infilled in the HADCRUT4 product. That is, if a 5-deg latitude by 5-deg longitude grid does not have a temperature anomaly value in a given month, it is left blank. Blank grids are indirectly assigned the average values for their respective hemispheres before the hemispheric values are merged. The HADCRUT4 reconstruction is described in the Morice et al (2012) paper here. The CRUTEM4 product is described in Jones et al (2012) here. And the HadSST3 reconstruction is presented in the 2-part Kennedy et al (2012) paper here and here. The UKMO uses the base years of 1961-1990 for anomalies. The monthly values of the HADCRUT4 product can be found here.

Update (Lags One Month): The September 2016 HADCRUT4 global temperature anomaly is +0.71 deg C. See Figure 3. It had a downtick from August to September 2016, a decrease of about -0.05 deg C.

Figure 3 – HADCRUT4

COMPARISONS

The GISS, HADCRUT4 and NCEI global surface temperature anomalies and the RSS and UAH lower troposphere temperature anomalies are compared in the next three time-series graphs. Figure 6 compares the five global temperature anomaly products starting in 1979. Again, due to the timing of this post, the HADCRUT4 and NCEI updates lag the UAH, RSS, and GISS products by a month. For those wanting a closer look at the more recent wiggles and trends, Figure 7 starts in 1998, which was the start year used by von Storch et al (2013) Can climate models explain the recent stagnation in global warming? They, of course, found that the CMIP3 (IPCC AR4) and CMIP5 (IPCC AR5) models could NOT explain the recent slowdown in warming, but that was before NOAA manufactured warming with their new ERSST.v4 reconstruction…and before the strong El Niño of 2015/16. Figure 8 starts in 2001, which was the year Kevin Trenberth chose for the start of the warming slowdown in his RMS article Has Global Warming Stalled?

Because the suppliers all use different base years for calculating anomalies, I’ve referenced them to a common 30-year period: 1981 to 2010. Referring to their discussion under FAQ 9 here, according to NOAA:

This period is used in order to comply with a recommended World Meteorological Organization (WMO) Policy, which suggests using the latest decade for the 30-year average.

The impacts of the unjustifiable, excessive adjustments to the ERSST.v4 reconstruction are visible in the two shorter-term comparisons, Figures 7 and 8. That is, the short-term warming rates of the new NCEI and GISS reconstructions are noticeably higher than the HADCRUT4 data. See the June 2015 update for the trends before the adjustments.

Figure 6 – Comparison Starting in 1979

#####

Figure 7 – Comparison Starting in 1998

#####

Figure 8 – Comparison Starting in 2001

Note also that the graphs list the trends of the CMIP5 multi-model mean (historic through 2005 and RCP8.5 forcings afterwards), which are the climate models used by the IPCC for their 5th Assessment Report. The metric presented for the models is surface temperature, not lower troposphere.

AVERAGE

Figure 9 presents the average of the GISS, HADCRUT and NCEI land plus sea surface temperature anomaly reconstructions and the average of the RSS and UAH lower troposphere temperature composites. Again because the HADCRUT4 and NCEI products lag one month in this update, the most current monthly average only includes the GISS product.

As noted above, the models in this post are represented by the CMIP5 multi-model mean (historic through 2005 and RCP8.5 forcings afterwards), which are the climate models used by the IPCC for their 5th Assessment Report.

Considering the uptick in surface temperatures in 2014, 2015 and now 2016 (see the posts here and here), government agencies that supply global surface temperature products have been touting “record high” combined global land and ocean surface temperatures. Alarmists happily ignore the fact that it is easy to have record high global temperatures in the midst of a hiatus or slowdown in global warming, and they have been using the recent record highs to draw attention away from the difference between observed global surface temperatures and the IPCC climate model-based projections of them.

There are a number of ways to present how poorly climate models simulate global surface temperatures. Normally they are compared in a time-series graph. See the example in Figure 10. In that example, the UKMO HadCRUT4 land+ocean surface temperature reconstruction is compared to the multi-model mean of the climate models stored in the CMIP5 archive, which was used by the IPCC for their 5th Assessment Report. The reconstruction and model outputs have been smoothed with 61-month running-mean filters to reduce the monthly variations. The climate science community commonly uses a 5-year running-mean filter (basically the same as a 61-month filter) to minimize the impacts of El Niño and La Niña events, as shown on the GISS webpage here. Using a 5-year running mean filter has been commonplace in global temperature-related studies for decades. (See Figure 13 here from Hansen and Lebedeff 1987 Global Trends of Measured Surface Air Temperature.) Also, the anomalies for the reconstruction and model outputs have been referenced to the period of 1880 to 2013 so not to bias the results. That is, by using the almost the full term of the data, no one with the slightest bit of common sense can claim I’ve cherry picked the base years for anomalies with this comparison.

Figure 10

It’s very hard to overlook the fact that, over the past decade, climate models are simulating way too much warming…even with the small recent El Niño-related uptick in the data.

Another way to show how poorly climate models perform is to subtract the observations-based reconstruction from the average of the model outputs (model mean). We first presented and discussed this method using global surface temperatures in absolute form. (See the post On the Elusive Absolute Global Mean Surface Temperature – A Model-Data Comparison.) The graph below shows a model-data difference using anomalies, where the data are represented by the UKMO HadCRUT4 land+ocean surface temperature product and the model simulations of global surface temperature are represented by the multi-model mean of the models stored in the CMIP5 archive. Like Figure 10, to assure that the base years used for anomalies did not bias the graph, the full term of the graph (1880 to 2013) was used as the reference period.

In this example, we’re illustrating the model-data differences smoothed with a 61-month running mean filter. (You’ll notice I’ve eliminated the monthly data from Figure 11. Example here. Alarmists can’t seem to grasp the purpose of the widely used 5-year (61-month) filtering, which as noted above is to minimize the variations due to El Niño and La Niña events and those associated with catastrophic volcanic eruptions.)

Figure 11

The difference now between models and data is almost worst-case, comparable to the difference at about 1910.

There was also a major difference, but of the opposite sign, in the late 1880s. That difference decreases drastically from the 1880s and switches signs by the 1910s. The reason: the models do not properly simulate the observed cooling that takes place at that time. Because the models failed to properly simulate the cooling from the 1880s to the 1910s, they also failed to properly simulate the warming that took place from the 1910s until the 1940s. (See Figure 12 for confirmation.) That explains the long-term decrease in the difference during that period and the switching of signs in the difference once again. The difference cycles back and forth, nearing a zero difference in the 1980s and 90s, indicating the models are tracking observations better (relatively) during that period. And from the 1990s to present, because of the slowdown in warming, the difference has increased to greatest value ever…where the difference indicates the models are showing too much warming.

It’s very easy to see the recent record-high global surface temperatures have had a tiny impact on the difference between models and observations.

Yet another way to show how poorly climate models simulate surface temperatures is to compare 30-year running trends of global surface temperature data and the model-mean of the climate model simulations of it. See Figure 12. In this case, we’re using the global GISS Land-Ocean Temperature Index for the data. For the models, once again we’re using the model-mean of the climate models stored in the CMIP5 archive with historic forcings to 2005 and worst case RCP8.5 forcings since then.

Figure 12

There are numerous things to note in the trend comparison. First, there is a growing divergence between models and data starting in the early 2000s. The continued rise in the model trends indicates global surface warming is supposed to be accelerating, but the data indicate little to no acceleration since then. Second, the plateau in the data warming rates begins in the early 1990s, indicating that there has been very little acceleration of global warming for more than 2 decades. This suggests that there MAY BE a maximum rate at which surface temperatures can warm. Third, note that the observed 30-year trend ending in the mid-1940s is comparable to the recent 30-year trends. (That, of course, is a function of the new NOAA ERSST.v4 data used by GISS.) Fourth, yet that high 30-year warming ending about 1945 occurred without being caused by the forcings that drive the climate models. That is, the climate models indicate that global surface temperatures should have warmed at about a third that fast if global surface temperatures were dictated by the forcings used to drive the models. In other words, if the models can’t explain the observed 30-year warming ending around 1945, then the warming must have occurred naturally. And that, in turns, generates the question: how much of the current warming occurred naturally? Fifth, the agreement between model and data trends for the 30-year periods ending in the 1960s to about 2000 suggests the models were tuned to that period or at least part of it. Sixth, going back further in time, the models can’t explain the cooling seen during the 30-year periods before the 1920s, which is why they fail to properly simulate the warming in the early 20th Century.

One last note, the monumental difference in modeled and observed warming rates at about 1945 confirms my earlier statement that the models can’t simulate the warming that occurred during the early warming period of the 20th Century.

MONTHLY SEA SURFACE TEMPERATURE UPDATE

The most recent sea surface temperature update can be found here. The satellite-enhanced sea surface temperature composite (Reynolds OI.2) are presented in global, hemispheric and ocean-basin bases.

RECENT RECORD HIGHS

We discussed the recent record-high global sea surface temperatures for 2014 and 2015 and the reasons for them in General Discussions 2 and 3 of my recent free ebook On Global Warming and the Illusion of Control (25MB). The book was introduced in the post here (cross post at WattsUpWithThat is here).

]]>https://bobtisdale.wordpress.com/2016/11/16/october-2016-global-surface-landocean-and-lower-troposphere-temperature-anomaly-update/feed/2bobtisdale04-uah-tlt05-rss-tlt01-giss-loti02-ncei03-hadcrut406-comprison-1979-start07-comparison-1998-start08-comparison-2001-start09-averages-surface-and-tlt10-model-data-time-series11-model-data-difference12-model-data-30-year-trendsSomething to Keep an Eye On – The Large Blue Ribbon of Below-Normal Sea Surface Temperatures in the North Pacifichttps://bobtisdale.wordpress.com/2016/11/10/something-to-keep-an-eye-on-the-large-blue-ribbon-of-below-normal-sea-surface-temperatures-in-the-north-pacific/
https://bobtisdale.wordpress.com/2016/11/10/something-to-keep-an-eye-on-the-large-blue-ribbon-of-below-normal-sea-surface-temperatures-in-the-north-pacific/#commentsThu, 10 Nov 2016 12:24:38 +0000http://bobtisdale.wordpress.com/?p=11394Continue reading →]]>The weather pattern that created Siberia’s well-below-normal land surface air temperatures in October 2016 …

…has apparently extended eastward. It is now influencing the sea surface temperatures of the North Pacific and creating an atypical large swath of cooler-than-normal observations that stretches almost fully from eastern Asia to western North America. See Animation 1, which is a gif animation of daily sea surface temperature anomaly maps from CMC Environment Canada for the past 30 days. Depending on your browser, you may need to click on the animation.

Animation 1

If those below-normal anomalies persist in the North Pacific, they should influence reported global sea surface temperatures in coming months. Then again, they could well result from a short-term weather pattern like the one that caused the recent resurrection and demise of THE BLOB.

The following is a Global map of Reynolds OI.v2 Sea Surface Temperature (SST) anomalies for October 2016. It was downloaded from the KNMI Climate Explorer. The contour range was set to -2.5 to +2.5 deg C and the anomalies are referenced to the WMO-preferred period of 1981-2010.

October 2016 Sea Surface Temperature (SST) Anomalies Map

(Global SST Anomaly = +0.32 deg C)

MONTHLY GLOBAL OVERVIEW

The global Sea Surface Temperature anomaly for October 2016 shows basically no change since September. A downtick in the Northern Hemisphere (-0.05 deg C) was countered by an uptick in the Southern Hemisphere (+0.03 deg C). Last month, the North Pacific was the basin to show the most cooling (-0.07 deg C), while the South Pacific (+0.05 deg C) and the Southern Ocean (+0.06 deg C) showed the most warming. Monthly sea surface temperature anomalies for the NINO3.4 continue to show weak La Niña conditions.

The monthly Global Sea Surface Temperature anomalies are presently at +0.32 deg C, referenced to the WMO-preferred base years of 1981 to 2010.

(1)Global Sea Surface Temperature Anomalies

Monthly Change = 0.00 deg C

THE EQUATORIAL PACIFIC

The monthly NINO3.4 Sea Surface Temperature anomalies for October 2016 are continuing to decline and continue to be below the threshold of a La Niña. They were at -0.74 deg C, a decrease since the prior month…having declined about -0.12 deg C since September. (Also see the Weekly data shown near the end of the post.)

(2) NINO3.4 Sea Surface Temperature Anomalies

(5S-5N, 170W-120W)

Monthly Change = -0.12 deg C

####################################

The sea surface temperature anomalies for the NINO3.4 region in the east-central equatorial Pacific (5S-5N, 170E-120E) are a commonly used index for the strength, frequency and duration of El Niño and La Nina events. We keep an eye on the sea surface temperatures there because El Niño and La Niña events are the primary cause of the yearly variations in global sea surface temperatures AND they are the primary cause of the long-term warming of global sea surface temperatures over the past 30+ years. See the discussion of the East Pacific versus the Rest-of-the-World that follows.We present NINO3.4 sea surface temperature anomalies in monthly and weekly formats in these updates.

Also see the weekly values toward the end of the post.

INITIAL NOTES

Note 1: I’ve downloaded the Reynolds OI.v2 data from the KNMI Climate Explorer, using the base years of 1981-2010. The updated base years help to reduce the seasonal components in the ocean-basin subsets—they don’t eliminate those seasonal components, but they reduce them.

The East Pacific Ocean also stands out in the trend map linked above. Some portions of its surfaces warmed and others cooled. It comes as no surprise then that the linear trend of the East Pacific (90S-90N, 180-80W) Sea Surface Temperature anomalies since the start of the Reynolds OI.v2 composite is so low. With the strong El Nino conditions in the eastern tropical Pacific and The Blob, it has acquired a slight positive trend, but it’s still far below the approximate +0.15 deg C/decade warming rate predicted by the CMIP5 climate models. Please see Figure 19 in the post Maybe the IPCC’s Modelers Should Try to Simulate Earth’s Oceans. (Note that the region also includes portions of the Arctic and Southern Oceans.) That is, there has been little warming of the sea surfaces of the East Pacific (from pole to pole) in 3-plus decades. The East Pacific is not a small region. It represents about 33% of the surface area of the global oceans.

That leaves the largest region of the trend map, which includes the South Atlantic, the Indian and West Pacific Oceans, with the corresponding portions of the Arctic and Southern Oceans. Sea surface temperatures there warmed in very clear steps, in response to the significant 1986/87/88 and 1997/98 El Niño/La Niña events. It also appears as though the sea surface temperature anomalies of this subset have made another upward shift in response to the 2009/10 El Niño and 2010/11 La Niña events. I further described the ENSO-related processes that cause these upward steps in the recent post Answer to the Question Posed at Climate Etc.: By What Mechanism Does an El Niño Contribute to Global Warming?

As you’ll note, the values for the South Atlantic, Indian and West Pacific Oceans appear now to be responding to the El Nino. And it appears we are seeing another El Niño-related uptick.

NOTE: I have updated the above illustration and following discussion, because NOAA has recently revised their Oceanic NINO Index…once again. They’ve used the base years of 1986-2015 for the most recent data, which has resurrected the 2014/15 El Niño.

The periods used for the average temperature anomalies for the South Atlantic-Indian-West Pacific subset between the significant El Niño events of 1982/83, 1986/87/88, 1997/98, 2009/10 and 2015/16 are determined as follows. Using the most recent NOAA Oceanic Nino Index (ONI) for the official months of those El Niño events, I shifted (lagged) those El Niño periods by six months to accommodate the lag between NINO3.4 SST anomalies and the response of the South Atlantic-Indian-West Pacific Oceans, then deleted the South Atlantic-Indian-West Pacific values that correspond to those significant El Niño events. I then averaged the South Atlantic-Indian-West Pacific Oceans sea surface temperature anomalies between those El Niño-related gaps.

You’ll note I’ve ended the updates for the period after the 2009-10 El Niño. That was done to accommodate the expected response to the 2015/16 El Niño.

The Sea Surface Temperature anomalies of the East Pacific Ocean, or approximately 33% of the surface area of the global oceans, have shown comparatively little long-term warming since 1982 based on the linear trend. And between upward shifts, the Sea Surface Temperature anomalies for the South Atlantic-Indian-West Pacific subset (about 52.5% of the global ocean surface area) remain relatively flat, though they actually cool slightly. Anthropogenic forcings are said to be responsible for most of the rise in global surface temperatures over this period, but the Sea Surface Temperature anomaly graphs of those regions discussed above prompt a two-part question: Since 1982, what anthropogenic global warming processes would overlook the sea surface temperatures of 33% of the global oceans and have an impact on the other 52% but only during the months of the significant El Niño events of 1986/87/88, 1997/98 and 2009/10?

The MONTHLY graphs illustrate raw monthly OI.v2 sea surface temperature anomalies from November 1981 to October 2016, as it is presented by the KNMI Climate Explorer. While NOAA uses the base years of 1971-2000 for this product, those base years cannot be used at the KNMI Climate Explorer because they extend before the start year of the product. (NOAA had created a special climatology for the Reynolds OI.v2 product.) I’ve referenced the anomalies to the period of 1981 to 2010, which is actually 1982 to 2010 for most months.

You’ll note that I included a comparison of the evolutions of the NINO3.4 sea surface temperature anomalies for the 1997/98 and 2015/16 El Niños. Just wanted to show that the transition this year toward La Niña has lagged behind the transition in 1998.

MODEL-DATA COMPARISON: To counter the nonsensical “Just what AGW predicts” rantings of alarmists about the “record-high” global sea surface temperatures in 2014 and 2015, I’ve added a model-data comparison of satellite-era global sea surface temperatures to these monthly updates. See the example below. The models are represented the multi-model ensemble-member mean of the climate models stored in the CMIP5 archive, which was used by the IPCC for their 5th Assessment Report. For further information on the use of the model mean, see the post here. For most models, historic forcings run through 2005 (2012 for others) and the middle-of-the-road RCP6.0 forcings are used after in this comparison. The data are represented by NOAA’s Optimum Interpolation Sea Surface Temperature data, version 2—a.k.a. Reynolds OI.v2—which is NOAA’s best. The model outputs and data have been shifted so that their trend lines begin at “zero” anomaly for the (November, 1981) start month of this composite. That “zeroing” helps to highlight how poorly the models simulate the warming of the ocean surfaces…noticeably higher than the observed warming rate. Both the Reynolds OI.v2 values and the model outputs of their simulations of sea surface temperature (TOS) are available to the public at the KNMI Climate Explorer.

000 – Model-Data Comparison

####################################

THE BLOB

We discussed the demise of the short-term reappearance of The Blob in the post THE BLOB has Dissipated. I’ll continue to present the sea surface temperature anomalies for The Blob region for one more month to show the enormous decline there since September.

(16) The Blob

(40N-50N, 150W-130W)

Monthly Change = -1.19 deg C

Note: I’ve changed the coordinates for The Blob to 40N-50N, 150W-130W to agree with those used in the NOAA/NCEP Monthly Ocean Briefing. I had been using the coordinates of 35N-55N, 150W-125W for The Blob.

HURRICANE MAIN DEVELOPMENT REGION

The sea surface temperatures of the tropical North Atlantic are one of the primary factors that contribute to the development and maintenance of hurricanes. I’ve recently added to the update the sea surface temperatures and anomalies for the hurricane main development region of the North Atlantic. It is often represented by the coordinates of 10N-20N, 80W-20W. While hurricanes tend to form there, they can also form outside it. While sea surfaces for the main development region are warmer than “normal”, there was nothing unusual about those levels in October.

(17) Hurricane Main Development Region

(10N-20N, 80W-20W)

Monthly Change (Anomalies) = 0.00 deg C

I’ve also included a graph of the October sea surface temperatures (not anomalies) for the Main Development Region. It confirms that sea surface temperatures there are not unusually warm and that sea surface temperatures of the Main Development Region are above the 26 deg C threshold for hurricane formation, as they normally are during October.

INTERESTED IN LEARNING MORE ABOUT HOW DATA SUGGEST THE GLOBAL OCEANS WARMED NATURALLY?

Why should you be interested? The hypothesis of manmade global warming depends on manmade greenhouse gases being the cause of the recent warming. But the sea surface temperature record indicates El Niño and La Niña events are responsible for the warming of global sea surface temperature anomalies over the past 32 years, not manmade greenhouse gases. Scroll back up to the discussion of the East Pacific versus the Rest of the World. I’ve searched sea surface temperature records for more than 4 years, and I can find no evidence of an anthropogenic greenhouse gas signal. That is, the warming of the global oceans has been caused by Mother Nature, not anthropogenic greenhouse gases.

My e-book (pdf) about the phenomena called El Niño and La Niña is titled Who Turned on the Heat? with the subtitle The Unsuspected Global Warming Culprit, El Niño Southern Oscillation. It is intended for persons (with or without technical backgrounds) interested in learning about El Niño and La Niña events and in understanding the natural causes of the warming of our global oceans for the past 30 years. Because land surface air temperatures simply exaggerate the natural warming of the global oceans over annual and multidecadal time periods, the vast majority of the warming taking place on land is natural as well. The book is the product of years of research of the satellite-era sea surface temperature data that’s available to the public via the internet. It presents how the data accounts for its warming—and there are no indications the warming was caused by manmade greenhouse gases. None at all.

The monthly Sea Surface Temperature (SST) anomalies, the map and model outputs used in this post are available from the KNMI Climate Explorer.

]]>https://bobtisdale.wordpress.com/2016/11/08/october-2016-sea-surface-temperature-sst-anomaly-update/feed/1bobtisdale00-ssta-map01-global-ssta02-nino3-4-ssta03-east-pacific-ssta04-s-atl-indian-w-pac-ssta05-n-hem-ssta06-s-hem-ssta07-n-atl-ssta08-s-atl-ssta09-pac-ssta10-n-pac-ssta11-s-pac-ssta12-indian-ssta13-arctic-ssta14-southern-ssta15-weekly-nino3-4-ssta000-model-data-comparison16-the-blob-ssta17-mdr-sstaPeer Review Is Bunkhttps://bobtisdale.wordpress.com/2016/10/27/peer-review-is-bunk/
https://bobtisdale.wordpress.com/2016/10/27/peer-review-is-bunk/#commentsThu, 27 Oct 2016 19:24:02 +0000http://bobtisdale.wordpress.com/2016/10/27/peer-review-is-bunk/Big Picture News, Informed Analysis: A report I wrote for the Global Warming Policy Foundation was released today. It explains that peer-reviewed research is as likely to be wrong as right. Basing public policy on findings that…]]>

A report I wrote for the Global Warming Policy Foundation was released today. It explains that peer-reviewed research is as likely to be wrong as right. Basing public policy on findings that haven’t yet been reproduced is nuts.

a marvelous cartoon by Josh graces the cover of my report

It’s time to slam on the brakes, folks. In recent decades, governments have justified all manner of guidelines, taxes, laws, and public awareness campaigns by claiming that a certain course of action is indicated by ‘science.’ We’re repeatedly told that ‘peer-reviewed’ science has determined X, and that society should therefore do Y.

But here’s the dirty little secret: the peer review process tells us almost nothing. It’s merely a sniff test. A couple of people briefly examine a research paper. Using entirely subjective criteria they decide that it kind of makes sense, that it must be right because it confirms their…

The Blob was the name given to the area of elevated sea surface temperature anomalies in the eastern extratropical North Pacific. It formed in 2013, coupling with a ridge of high pressure that impacted weather patterns across North America into 2015. The Blob was the primary contributor to the reported record high global sea surface temperature anomalies in 2014, and contributed to the record highs in 2015 along with the 2015 portion of the 2014/15/16 El Niño. (See General Discussions 2 and 3 of my free ebook On Global Warming and the Illusion of Control – Part 1. Also see the series of posts about The Blob for additional general information.)

After a noticeable drop in the sea surface temperature anomalies for that region of the eastern extratropical North Pacific that lasted from late-2015 through mid-2016, the Blob reemerged in August, with the sea surface temperature anomalies peaking in September for 2016. See Animation 2, which includes daily sea surface temperature anomaly maps running from June 1, 2016 through today. (Depending on your browser, you may need to click on the animation.)

As shown, The Blob began to decay noticeably in early-to-mid October and has, now, basically disappeared from its normal location, though there are still elevated sea surface temperature anomalies in the eastern and central extratropical North Pacific.

We should expect a noticeable decline in the sea surface temperature anomalies this month in The Blob region, and in the North Pacific as a whole, when the monthly sea surface temperature data for October are published early next month.

Will The Blob disappear for good in the future or will it reappear annually during the boreal summer months as part of a new seasonal cycle? Only time will tell.

]]>https://bobtisdale.wordpress.com/2016/10/27/the-blob-has-dissipated/feed/11bobtisdaleanimation-1animation-2September 2016 Global Surface (Land+Ocean) and Lower Troposphere Temperature Anomaly Updatehttps://bobtisdale.wordpress.com/2016/10/18/september-2016-global-surface-landocean-and-lower-troposphere-temperature-anomaly-update/
https://bobtisdale.wordpress.com/2016/10/18/september-2016-global-surface-landocean-and-lower-troposphere-temperature-anomaly-update/#commentsTue, 18 Oct 2016 11:29:20 +0000http://bobtisdale.wordpress.com/?p=11346Continue reading →]]>This post provides updates of the values for the three primary suppliers of global land+ocean surface temperature reconstructions—GISS through September 2016 and HADCRUT4 and NCEI (formerly NCDC) through August 2016—and of the two suppliers of satellite-based lower troposphere temperature composites (RSS and UAH) through September 2016. It also includes a few model-data comparisons.

This is simply an update, but it includes a good amount of background information for those new to the datasets. Because it is an update, there is no overview or summary for this post. There are, however, summaries for the individual updates. So for those familiar with the datasets, simply fast-forward to the graphs and read the summaries under the heading of “Update”.

(I’m still on holiday, so I may not get a chance to respond to comments.)

INITIAL NOTES:

We discussed and illustrated the impacts of the adjustments to surface temperature data in the posts:

The NOAA NCEI product is the new global land+ocean surface reconstruction with the manufactured warming presented in Karl et al. (2015). For summaries of the oddities found in the new NOAA ERSST.v4 “pause-buster” sea surface temperature data see the posts:

Even though the changes to the ERSST reconstruction since 1998 cannot be justified by the night marine air temperature product that was used as a reference for bias adjustments (See comparison graph here), and even though NOAA appears to have manipulated the parameters (tuning knobs) in their sea surface temperature model to produce high warming rates (See the post here), GISS also switched to the new “pause-buster” NCEI ERSST.v4 sea surface temperature reconstruction with their July 2015 update.

The UKMO also recently made adjustments to their HadCRUT4 product, but they are minor compared to the GISS and NCEI adjustments.

We’re using the UAH lower troposphere temperature anomalies Release 6.5 for this post even though it’s in beta form. And for those who wish to whine about my portrayals of the changes to the UAH and to the GISS and NCEI products, see the post here.

The GISS LOTI surface temperature reconstruction and the two lower troposphere temperature composites are for the most recent month. The HADCRUT4 and NCEI products lag one month.

Much of the following text is boilerplate that has been updated for all products. The boilerplate is intended for those new to the presentation of global surface temperature anomalies.

Most of the graphs in the update start in 1979. That’s a commonly used start year for global temperature products because many of the satellite-based temperature composites start then.

We discussed why the three suppliers of surface temperature products use different base years for anomalies in chapter 1.25 – Many, But Not All, Climate Metrics Are Presented in Anomaly and in Absolute Forms of my free ebook On Global Warming and the Illusion of Control – Part 1 (25MB).

Since the July 2015 update, we’re using the UKMO’s HadCRUT4 reconstruction for the model-data comparisons using 61-month filters.

For a continued change of pace, let’s start with the lower troposphere temperature data. I’ve left the illustration numbering as it was in the past when we began with the surface-based data.

UAH LOWER TROPOSPHERE TEMPERATURE ANOMALY COMPOSITE (UAH TLT)

Special sensors (microwave sounding units) aboard satellites have orbited the Earth since the late 1970s, allowing scientists to calculate the temperatures of the atmosphere at various heights above sea level (lower troposphere, mid troposphere, tropopause and lower stratosphere). The atmospheric temperature values are calculated from a series of satellites with overlapping operation periods, not from a single satellite. Because the atmospheric temperature products rely on numerous satellites, they are known as composites. The level nearest to the surface of the Earth is the lower troposphere. The lower troposphere temperature composite include the altitudes of zero to about 12,500 meters, but are most heavily weighted to the altitudes of less than 3000 meters. See the left-hand cell of the illustration here.

Note: RSS recently release new versions of the mid-troposphere temperature and lower stratosphere temperature (TLS) products. So far, their lower troposphere temperature product has not been updated to this new version.

Introduction: The GISS Land Ocean Temperature Index (LOTI) reconstruction is a product of the Goddard Institute for Space Studies. Starting with the June 2015 update, GISS LOTI uses the new NOAA Extended Reconstructed Sea Surface Temperature version 4 (ERSST.v4), the pause-buster reconstruction, which also infills grids without temperature samples. For land surfaces, GISS adjusts GHCN and other land surface temperature products via a number of methods and infills areas without temperature samples using 1200km smoothing. Refer to the GISS description here. Unlike the UK Met Office and NCEI products, GISS masks sea surface temperature data at the poles, anywhere seasonal sea ice has existed, and they extend land surface temperature data out over the oceans in those locations, regardless of whether or not sea surface temperature observations for the polar oceans are available that month. Refer to the discussions here and here. GISS uses the base years of 1951-1980 as the reference period for anomalies. The values for the GISS product are found here. (I archived the former version here at the WaybackMachine.)

Update: The September 2016 GISS global temperature anomaly is +0.91 deg C. According to the GISS LOTI data, global surface temperature anomalies made a downtick in September, a -0.06 deg C decrease.

Figure 1 – GISS Land-Ocean Temperature Index

NCEI GLOBAL SURFACE TEMPERATURE ANOMALIES (LAGS ONE MONTH)

NOTE: The NCEI only produces the product with the manufactured-warming adjustments presented in the paper Karl et al. (2015). As far as I know, the former version of the reconstruction is no longer available online. For more information on those curious NOAA adjustments, see the posts:

Update (Lags One Month): The August 2016 NCEI global land plus sea surface temperature anomaly was +0.92 deg C. See Figure 2. It increased (a rise of about +0.05 deg C) since July 2016.

Figure 2 – NCEI Global (Land and Ocean) Surface Temperature Anomalies

UK MET OFFICE HADCRUT4 (LAGS ONE MONTH)

Introduction: The UK Met Office HADCRUT4 reconstruction merges CRUTEM4 land-surface air temperature product and the HadSST3 sea-surface temperature (SST) reconstruction. CRUTEM4 is the product of the combined efforts of the Met Office Hadley Centre and the Climatic Research Unit at the University of East Anglia. And HadSST3 is a product of the Hadley Centre. Unlike the GISS and NCEI reconstructions, grids without temperature samples for a given month are not infilled in the HADCRUT4 product. That is, if a 5-deg latitude by 5-deg longitude grid does not have a temperature anomaly value in a given month, it is left blank. Blank grids are indirectly assigned the average values for their respective hemispheres before the hemispheric values are merged. The HADCRUT4 reconstruction is described in the Morice et al (2012) paper here. The CRUTEM4 product is described in Jones et al (2012) here. And the HadSST3 reconstruction is presented in the 2-part Kennedy et al (2012) paper here and here. The UKMO uses the base years of 1961-1990 for anomalies. The monthly values of the HADCRUT4 product can be found here.

Update (Lags One Month): The August 2016 HADCRUT4 global temperature anomaly is +0.78 deg C. See Figure 3. It had an uptick from July to August 2016, an increase of about +0.04 deg C.

Figure 3 – HADCRUT4

COMPARISONS

The GISS, HADCRUT4 and NCEI global surface temperature anomalies and the RSS and UAH lower troposphere temperature anomalies are compared in the next three time-series graphs. Figure 6 compares the five global temperature anomaly products starting in 1979. Again, due to the timing of this post, the HADCRUT4 and NCEI updates lag the UAH, RSS, and GISS products by a month. For those wanting a closer look at the more recent wiggles and trends, Figure 7 starts in 1998, which was the start year used by von Storch et al (2013) Can climate models explain the recent stagnation in global warming? They, of course, found that the CMIP3 (IPCC AR4) and CMIP5 (IPCC AR5) models could NOT explain the recent slowdown in warming, but that was before NOAA manufactured warming with their new ERSST.v4 reconstruction…and before the strong El Niño of 2015/16. Figure 8 starts in 2001, which was the year Kevin Trenberth chose for the start of the warming slowdown in his RMS article Has Global Warming Stalled?

Because the suppliers all use different base years for calculating anomalies, I’ve referenced them to a common 30-year period: 1981 to 2010. Referring to their discussion under FAQ 9 here, according to NOAA:

This period is used in order to comply with a recommended World Meteorological Organization (WMO) Policy, which suggests using the latest decade for the 30-year average.

The impacts of the unjustifiable, excessive adjustments to the ERSST.v4 reconstruction are visible in the two shorter-term comparisons, Figures 7 and 8. That is, the short-term warming rates of the new NCEI and GISS reconstructions are noticeably higher than the HADCRUT4 data. See the June 2015 update for the trends before the adjustments.

Figure 6 – Comparison Starting in 1979

###########

Figure 7 – Comparison Starting in 1998

#####

Figure 8 – Comparison Starting in 2001

Note also that the graphs list the trends of the CMIP5 multi-model mean (historic through 2005 and RCP8.5 forcings afterwards), which are the climate models used by the IPCC for their 5th Assessment Report. The metric presented for the models is surface temperature, not lower troposphere.

AVERAGE

Figure 9 presents the average of the GISS, HADCRUT and NCEI land plus sea surface temperature anomaly reconstructions and the average of the RSS and UAH lower troposphere temperature composites. Again because the HADCRUT4 and NCEI products lag one month in this update, the most current monthly average only includes the GISS product.

As noted above, the models in this post are represented by the CMIP5 multi-model mean (historic through 2005 and RCP8.5 forcings afterwards), which are the climate models used by the IPCC for their 5th Assessment Report.

Considering the uptick in surface temperatures in 2014, 2015 and now 2016 (see the posts here and here), government agencies that supply global surface temperature products have been touting “record high” combined global land and ocean surface temperatures. Alarmists happily ignore the fact that it is easy to have record high global temperatures in the midst of a hiatus or slowdown in global warming, and they have been using the recent record highs to draw attention away from the difference between observed global surface temperatures and the IPCC climate model-based projections of them.

There are a number of ways to present how poorly climate models simulate global surface temperatures. Normally they are compared in a time-series graph. See the example in Figure 10. In that example, the UKMO HadCRUT4 land+ocean surface temperature reconstruction is compared to the multi-model mean of the climate models stored in the CMIP5 archive, which was used by the IPCC for their 5th Assessment Report. The reconstruction and model outputs have been smoothed with 61-month running-mean filters to reduce the monthly variations. The climate science community commonly uses a 5-year running-mean filter (basically the same as a 61-month filter) to minimize the impacts of El Niño and La Niña events, as shown on the GISS webpage here. Using a 5-year running mean filter has been commonplace in global temperature-related studies for decades. (See Figure 13 here from Hansen and Lebedeff 1987 Global Trends of Measured Surface Air Temperature.) Also, the anomalies for the reconstruction and model outputs have been referenced to the period of 1880 to 2013 so not to bias the results. That is, by using the almost the full term of the data, no one with the slightest bit of common sense can claim I’ve cherry picked the base years for anomalies with this comparison.

Figure 10

It’s very hard to overlook the fact that, over the past decade, climate models are simulating way too much warming…even with the small recent El Niño-related uptick in the data.

Another way to show how poorly climate models perform is to subtract the observations-based reconstruction from the average of the model outputs (model mean). We first presented and discussed this method using global surface temperatures in absolute form. (See the post On the Elusive Absolute Global Mean Surface Temperature – A Model-Data Comparison.) The graph below shows a model-data difference using anomalies, where the data are represented by the UKMO HadCRUT4 land+ocean surface temperature product and the model simulations of global surface temperature are represented by the multi-model mean of the models stored in the CMIP5 archive. Like Figure 10, to assure that the base years used for anomalies did not bias the graph, the full term of the graph (1880 to 2013) was used as the reference period.

In this example, we’re illustrating the model-data differences smoothed with a 61-month running mean filter. (You’ll notice I’ve eliminated the monthly data from Figure 11. Example here. Alarmists can’t seem to grasp the purpose of the widely used 5-year (61-month) filtering, which as noted above is to minimize the variations due to El Niño and La Niña events and those associated with catastrophic volcanic eruptions.)

Figure 11

The difference now between models and data is almost worst-case, comparable to the difference at about 1910.

There was also a major difference, but of the opposite sign, in the late 1880s. That difference decreases drastically from the 1880s and switches signs by the 1910s. The reason: the models do not properly simulate the observed cooling that takes place at that time. Because the models failed to properly simulate the cooling from the 1880s to the 1910s, they also failed to properly simulate the warming that took place from the 1910s until the 1940s. (See Figure 12 for confirmation.) That explains the long-term decrease in the difference during that period and the switching of signs in the difference once again. The difference cycles back and forth, nearing a zero difference in the 1980s and 90s, indicating the models are tracking observations better (relatively) during that period. And from the 1990s to present, because of the slowdown in warming, the difference has increased to greatest value ever…where the difference indicates the models are showing too much warming.

It’s very easy to see the recent record-high global surface temperatures have had a tiny impact on the difference between models and observations.

Yet another way to show how poorly climate models simulate surface temperatures is to compare 30-year running trends of global surface temperature data and the model-mean of the climate model simulations of it. See Figure 12. In this case, we’re using the global GISS Land-Ocean Temperature Index for the data. For the models, once again we’re using the model-mean of the climate models stored in the CMIP5 archive with historic forcings to 2005 and worst case RCP8.5 forcings since then.

Figure 12

There are numerous things to note in the trend comparison. First, there is a growing divergence between models and data starting in the early 2000s. The continued rise in the model trends indicates global surface warming is supposed to be accelerating, but the data indicate little to no acceleration since then. Second, the plateau in the data warming rates begins in the early 1990s, indicating that there has been very little acceleration of global warming for more than 2 decades. This suggests that there MAY BE a maximum rate at which surface temperatures can warm. Third, note that the observed 30-year trend ending in the mid-1940s is comparable to the recent 30-year trends. (That, of course, is a function of the new NOAA ERSST.v4 data used by GISS.) Fourth, yet that high 30-year warming ending about 1945 occurred without being caused by the forcings that drive the climate models. That is, the climate models indicate that global surface temperatures should have warmed at about a third that fast if global surface temperatures were dictated by the forcings used to drive the models. In other words, if the models can’t explain the observed 30-year warming ending around 1945, then the warming must have occurred naturally. And that, in turns, generates the question: how much of the current warming occurred naturally? Fifth, the agreement between model and data trends for the 30-year periods ending in the 1960s to about 2000 suggests the models were tuned to that period or at least part of it. Sixth, going back further in time, the models can’t explain the cooling seen during the 30-year periods before the 1920s, which is why they fail to properly simulate the warming in the early 20th Century.

One last note, the monumental difference in modeled and observed warming rates at about 1945 confirms my earlier statement that the models can’t simulate the warming that occurred during the early warming period of the 20th Century.

MONTHLY SEA SURFACE TEMPERATURE UPDATE

The most recent sea surface temperature update can be found here. The satellite-enhanced sea surface temperature composite (Reynolds OI.2) are presented in global, hemispheric and ocean-basin bases.

RECENT RECORD HIGHS

We discussed the recent record-high global sea surface temperatures for 2014 and 2015 and the reasons for them in General Discussions 2 and 3 of my recent free ebook On Global Warming and the Illusion of Control (25MB). The book was introduced in the post here (cross post at WattsUpWithThat is here).

]]>https://bobtisdale.wordpress.com/2016/10/18/september-2016-global-surface-landocean-and-lower-troposphere-temperature-anomaly-update/feed/2bobtisdale04-uah-tlt05-rss-tlt01-giss-loti02-ncei03-hadcrut406-comparison-1979-start07-comparison-1998-start08-comparison-2001-start09-surface-and-tlt-averages10-model-data-time-series11-model-data-difference12-model-data-30-year-trendsThe Divergence between Surface and Lower Troposphere Global Temperature Datasets and its Implicationshttps://bobtisdale.wordpress.com/2016/10/14/the-divergence-between-surface-and-lower-troposphere-global-temperature-datasets-and-its-implications/
https://bobtisdale.wordpress.com/2016/10/14/the-divergence-between-surface-and-lower-troposphere-global-temperature-datasets-and-its-implications/#commentsFri, 14 Oct 2016 12:20:02 +0000http://bobtisdale.wordpress.com/?p=11338Continue reading →]]>I include a graph in my monthly global surface temperature and lower troposphere temperature anomaly updates that compares the average of the global surface land+ocean temperature anomaly products (from GISS, NCEI and UKMO) to the average of the global lower troposphere temperature anomaly products (from RSS and UAH). (See Figure 9 from the most recent August update for an example.) Because all of the suppliers use difference base years for their anomalies, I’ve recalculated the anomalies for all using the WMO-preferred reference period of 1981-2010.

Figure 1

My Figure 1 is similar to Figure 9 from those updates, but in it, I’ve also shown the linear trends for the global surface and lower troposphere temperature anomaly products. The linear trend, the warming rate, presented by the average surface-based products is noticeably higher than the average lower troposphere products. This, of course, according to Dr. Gavin Schmidt (head of NASA GISS), is the opposite of what the anthropogenic global warming hypothesis tells us is supposed to happen, which is that the lower troposphere is supposed to warm at a faster rate than the surface. See Screen Cap 1.

Screen Cap 1 (Click for full size)

BUT WHEN DO THE SURFACE AND LOWER TROPOSPHERE PRODUCTS BEGIN TO DIVERGE?

To determine this we need to look at the warming rates (linear trends) of the average surface and lower troposphere temperature data.

With a start year of 1979 and working backwards in time from 2015 to 1989, I had EXCEL calculate the annual linear trends of the average surface temperature and average lower troposphere temperature anomaly data. Table 1 shows the lower troposphere and surface temperature trends from 1979 to the listed end years of 2015 to 1989. With the exception of a few end years from the late 1990s to the early 2000s, the average lower troposphere temperature data have noticeably lower warming rates than the average surface temperature products. The similarities in the trends from the late 1990s through to the early 2000s are likely caused by the excessive response of the global lower troposphere temperatures to the 1997/98 El Niño.

Table 1

NOTE: For those new to the discussion, there are very fundamental reasons why the lower troposphere has an excessive response to a massive El Niño. The lower troposphere warms for two reasons during an El Nino. First, it warms because the Earth’s surface warms as a result of the El Nino. Second, the lower troposphere warms an additional amount because the El Niño’s higher sea surface temperatures in the tropical Pacific cause a tremendous amount of moisture to be evaporated from its surface and that moisture releases more heat to the troposphere after it rises into the atmosphere, condenses and forms clouds. [End note.]

Plainly, the 1997/98 El Niño appears to have caused a temporary alignment of the trends of the average surface and average lower troposphere temperature products. Regardless, the trends of the two metrics align at the end year of 1999, so we’ll use that as our breakpoint in this discussion. See Figure 2.

Figure 2

From 1979 to 1999, the trends of the two metrics are the same at 0.147 deg C/decade.

WHAT ARE THE TRENDS AFTERWARDS?

Figure 3 illustrates the linear trends of the average global surface temperature and the average global lower troposphere temperature products from January 2000 to now, August 2016. The average global surface temperature data almost double the warming rate of the average global lower troposphere temperature data during this period.

Figure 3

But according to the hypothesis of manmade greenhouse gas-driven global warming, the opposite is supposed to happen…the lower troposphere is supposed to be warming faster than the surface.

THREE POSSIBLE REASONS WHY DATA CONTRADICT HYPOTHESIS

Of course, there are three possible reasons why the global lower troposphere and surface temperature products do not agree with the hypothesis of human-induced global warming: